OpenClaw Local Models for 32 GB RAM

Thirty-two gigabytes gives you room to compare stronger quantized local models or use a longer context, but it is still a shared system budget. Do not allocate every available gigabyte to model weights.

OpenClaw’s official local-model runbook recommends substantially more hardware for a comfortable full agent loop. A 32 GB machine is best treated as a capable test and light-workload host; keep permissions narrow and a stronger reviewed fallback for tasks where local reliability is insufficient.

32 GB memory allocation

Use the additional headroom for one primary benefit at a time:

• a larger model with stronger instruction following;
• a less aggressive quantization;
• a longer working context;
• more room for browser, coding, or document tools.

Changing all four at once makes failures difficult to explain.

Establish a baseline

First run the compact model and context that would fit a 16 GB machine. Record latency, tool-call accuracy, and peak memory. Then change only the model or context and repeat the same fixtures.

openclaw models list --provider ollama
openclaw models set <ollama/model-id>
openclaw models status

Test the maximum working set

The important measurement is not whether the model loads. Run the longest representative conversation while the intended tools are open. Watch for swapping, out-of-memory termination, sharply increasing latency, and malformed calls late in the context.

Prefer repeatable gains

Keep the larger candidate only if it improves your actual task set. A model that gains chat fluency but loses structured tool reliability is a downgrade for an agent workflow.

Expected result

The chosen configuration keeps meaningful memory headroom at peak load and shows a repeatable improvement over the smaller baseline without reducing tool-call success.